Artificial Intelligence and the Tax Practitioner

Patrick Buckley; Elaine Doyle; Brendan McCarthy; Ruth Gilligan

Authors

Patrick Buckley Department of Management and Marketing, Kemmy Business School, University of Limerick, Ireland
Elaine Doyle Department of Accounting and Finance, Kemmy Business School, University of Limerick, Ireland
Brendan McCarthy Department of Accounting and Finance, Kemmy Business School, University of Limerick, Ireland
Ruth Gilligan PwC Dublin, Ireland

Keywords:

Artificial Intelligence, The Future of Tax, Tax Professionals, Emerging Technology

Abstract

The advent of artificial intelligence (AI) and machine learning (ML) has sparked concern that many jobs are at risk of automation. This paper contributes to this debate in the context of the tax practitioner. We describe a methodological approach that redefines the appropriate loci of analysis as a combination of the level of task and the career stage rather than focussing on the tax role at a macro level. We use these revised loci to perform a meta-analysis of existing studies in order to examine the role of the tax practitioner. The change in focus of analysis reveals a number of insights which have been heretofore obscured.

References

Abbott, A. (2005). Sociology of work and occupations. In N. J. Smelser & R. Swedberg (Eds.), The handbook of economic sociology (2nd ed., pp. 307-330). Princeton, NJ: Princeton University Press.

Aghion, P., & Howitt, P. (1994). Growth and unemployment. The Review of Economic Studies, 61(3), 477-494. https://doi.org/10.2307/2297900

Alm, J., Beebe, J., Kirsch, M. S., Marian, O., & Soled, J. A. (2020). New technologies and the evolution of tax compliance. Virginia Tax Review, 39(3), 287-356.

Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358-367. https://doi.org/10.1016/j.giq.2018.10.001

Anteby, M., Chan, C. K., & DiBenigno, J. (2016). Three lenses on occupations and professions in organizations: Becoming, doing, and relating. Academy of Management Annals, 10(1), 183-244. https://doi.org/10.5465/19416520.2016.1120962

Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis (OECD Social, Employment and Migration Working Papers, No. 189). https://doi.org/10.1787/5jlz9h56dvq7-en

Asimov, I. (1950). I, robot. Garden City, N.Y.: Doubleday & Company, Inc.

Autor, D. H, & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 103(5), 1553-1597. https://doi.org/10.1257/aer.103.5.1553

Autor, D., Mindell, D., & Reynolds, E. (2020). The work of the future: Building better jobs in an age of intelligent machines. Cambridge, MA: Massachusetts Institute of Technology.

Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L., Berriel, R., Paixāo, T. M., Mutz, F., de Paula Veronese, L., Oliveira-Santos, T., De Souza, A. F. (2020). Self-driving cars: A survey. Expert Systems with Applications, 165, Article 113816. https://doi.org/10.1016/j.eswa.2020.113816

Barton, K. (2020, December 17). Why tax was - and will remain - at the epicenter of COVID-19 recovery. EY.com. https://www.ey.com/en_gl/tax/why-tax-was-and-will-remain-atthe-epicenter-of-covid-19-recovery

Baum, S. D., Goertzel, B., & Goertzel, T. G. (2011). How long until human-level AI? Results from an expert assessment. Technological Forecasting and Social Change, 78(1), 185-195. https://doi.org/10.1016/j.techfore.2010.09.006

Bostrom, N. (2016). Superintelligence: Paths, dangers, strategies (reprint). Oxford, England: Oxford University Press.

Brynjolfsson, E., & McAfee, A. (2011). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Lexington, MA: Digital Frontier Press.

Clark, A. (2005). Intrinsic content, active memory and the extended mind. Analysis, 65(1), 1-11. https://doi.org/10.1093/analys/65.1.1

Crevier, D. (1993). AI: The tumultuous history of the search for artificial intelligence. New York, NY: Basic Books.

Dennett, D. C. (2017). From bacteria to Bach and back: The evolution of minds. New York, NY: W. W. Norton & Company.

Dobell, J. (2017, May 30). The future of tax technology is now. International Tax Review. https://www.internationaltaxreview.com/article/b1f7n77lbf6703/the-future-of-taxtechnology-is-now

Doyle, E. M., Frecknall-Hughes, J. F., & Glaister, K. W. (2009). Linking ethics and risk management in taxation: Evidence from an exploratory study in Ireland and the UK. Journal of Business Ethics, 86(2), 177-198. https://doi.org/10.1007/s10551-008-9842-9

Doyle, E., Frecknall-Hughes, J., & Summers, B. (2014). Ethics in tax practice: A study of the effect of practitioner firm size. Journal of Business Ethics, 122(4), 623-641. https://doi.org/10.1007/s10551-013-1780-5

EY Global. (2020a). How to build an intelligent tax function. https://www.ey.com/en_gl/tax/how-to-build-an-intelligent-tax-function

EY Global. (2020b). How artificial intelligence will empower the tax function. https://www.ey.com/en_gl/tax/how-artificial-intelligence-will-empower-the-tax-function

Ford, M. R. (2009). The lights in the tunnel: Automation, accelerating technology and the economy of the future. n.p.: Acculant Publishing.

Frecknall-Hughes, J., & Kirchler, E. (2015). Towards a general theory of tax practice. Social & Legal Studies, 24(2), 289-312. https://doi.org/10.1177/0964663915571787

Frecknall-Hughes, J., Moizer, P., Doyle, E., & Summers, B. (2017). An examination of ethical influences on the work of tax practitioners. Journal of Business Ethics, 146(4), 729-745. https://doi.org/10.1007/s10551-016-3037-6

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019

Fuei, L. K. (2017). Automation, computerization and future employment in Singapore. Journal of Southeast Asian Economies, 34(2), 388-399. https://doi.org/10.1355/ae34-2h

Goldin, C., & Katz, L. F. (1998). The origins of technology-skill complementarity. The Quarterly Journal of Economics, 113(3), 693-732.

Goos, M., Manning, A., & Salomons, A. (2009). Job polarization in Europe. American Economic Review, 99(2), 58-63. https://doi.org/10.1257/aer.99.2.58

Hahn, T.-A., & Ormeño Pérez, R. (2020). Tax professionals in the academic spotlight: A review of recent literature. Journal of Tax Administration, 6(1), 96-161.

Harari, Y. N. (2016). Homo Deus: A brief history of tomorrow. London, England: Harville Secker.

Hodson, R., & Sullivan, T. A. (2012). The social organization of work. Belmont, CA: Wadsworth Publishing Company.

Hoy, M. B. (2018). Alexa, Siri, Cortana, and more: An introduction to voice assistants. Medical Reference Services Quarterly, 37(1), 81-88. https://doi.org/10.1080/02763869.2018.1404391

Huang, B., & Yu, J. (2019). Leading digital technologies for coproduction: The case of “visit once” administrative service reform in Zhejiang Province, China. Journal of Chinese Political Science, 24(3), 513-532.

Joy, B. (2000). Why the future doesn’t need us. Wired magazine, 8(4), 238-262.

Keynes, J. M. (2010). The general theory of employment, interest and money. Kila, MT: Kessinger Publishing. (Original work published 1926).

Kurzweil, R. (2005). The singularity is near: When humans transcend biology. London, England: Viking Penguin.

Leontief, W. (1983). Technological advance, economic growth, and the distribution of income. Population and Development Review, 9(3), 403-410. https://doi.org/10.2307/1973315

Levy, F., & Murnane, R. J. (2013). Dancing with robots: Human skills for computerized work. Washington, D.C.: Third Way.

Li, J., Wang, X., & Wu, Y. (2020). Can government improve tax compliance by adopting advanced information technology? Evidence from the Golden Tax Project III in China. Economic Modelling, 93, 384-397. https://doi.org/10.1016/j.econmod.2020.08.009

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149-157. https://doi.org/10.1016/j.jsis.2015.08.002

Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., Sanghvi, S. (2017). Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages. n.p.: McKinsey & Company.

Marr, B., & Ward, M. (2019). Artificial intelligence in practice: How 50 successful companies used artificial intelligence and machine learning to solve problems. Chichester, England: John Wiley & Sons Ltd.

Muehlhauser, L., & Salamon, A. (2012). Intelligence explosion: Evidence and import. In A. H. Eden, J. H. Moor, J. H. Søraker, & E. Steinhart (Eds.), Singularity hypotheses: A scientific and philosophical assessment (pp. 15-42). Heidelberg, Germany: Springer-Verlag Berlin-Heidelberg.

Myeong, S., & Jung, Y. (2019). Administrative reforms in the fourth industrial revolution: The case of blockchain use. Sustainability, 11(14), 3971. https://doi.org/10.3390/su11143971

Nelson, A. J., & Irwin, J. (2014). “Defining what we do—All over again”: Occupational identity, technological change, and the librarian/internet-search relationship. Academy of Management Journal, 57(3), 892-928. https://doi.org/10.5465/amj.2012.0201

Nibbe, J. (2020, January 29). Should tax be keeping pace with transformation, or help shape it? EY.com. https://www.ey.com/en_uk/digital/should-tax-be-keeping-pace-withtransformation-or-shaping-it

Nilsson, N. J. (2009). The quest for artificial intelligence: A history of ideas and achievements. New York, NY: Cambridge University Press.

Organisation for Economic Co-operation and Development. (2008). Study into the role of tax intermediaries. Paris, France: OECD Publishing.

Parkins, D. (2017, 6th May). The world's most valuable resource is no longer oil, but data. The Economist. https://www.economist.com/leaders/2017/05/06/the-worlds-mostvaluable-resource-is-no-longer-oil-but-data

Penrose, R. (2002). The Emperor's new mind: Concerning computers, minds, and the laws of physics. Oxford, England: Oxford University Press.

PricewaterhouseCoopers (2017a). Bot. Me: A revolutionary partnership: How AI is pushing man and machine closer together. n.p.: PwC.

PricewaterhouseCoopers. (2017b). Tax function of the future: Upskilling the tax professional. PwC. https://www.pwc.com/us/en/services/tax/library/tax-function-upskilling-taxprofessional.html

PricewaterhouseCoopers. (2017c). The tax professional of the future. Staying relevant in changing times. np: PwC.

PricewaterhouseCoopers. (2019). How tax is leveraging AI—Including machine learning —in 2019. np: PwC.

Ricardo, D. (1821). On the principles of political economy and taxation. London, England: John Murray.

Rifkin, J. (1995). The end of work: The decline of the global labor force and the dawn of the post-market era. New York, NY: G. P. Putnam’s Sons.

Sorola, M., Karavidas, D., & Laheen, M. (2020). Addressing gender issues through the management of tax talent. Journal of Tax Administration, 6(1), 51-73.

Thuronyi, V., & Vanistendael, F. J. G. M. (1996). Regulation of tax professionals. In V.Thuronyi (Ed.), Tax law design and drafting (Vol. 1, pp.135-163). Washington D.C.: International Monetary Fund.

Van Maanen, J., & Schein, E. H. (1979). Toward a theory of organizational socialization. Research in Organizational Behavior, 1, 209-264.

White, S., Lacey, A., & Ardanaz-Badia, A. (2019). The probability of automation in England: 2011 and 2017. n.p.: Office for National Statistics.